System and method for estimating extracorporeal blood volume in a physical sample

ABSTRACT

One method for estimating the extracorporeal blood volume in a portion of a physical sample includes: comparing a portion of an image of the sample with a template image of known extracorporeal blood volume indicator; tagging the portion of the image of the sample with a blood volume indicator according to the template image that is matched to the portion of the image of the sample; and estimating the extracorporeal blood volume in at least a portion of the physical sample, associated with the portion of the image of the sample, according to the blood volume indicator.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 61/506,082, filed 9 Jul. 2011, U.S. ProvisionalPatent Application Ser. No. 61/646,818, filed 14 May 2012, and U.S.Provisional Patent Application Ser. No. 61/646,814, filed 14 May 2012,all of which are herein incorporated in their entireties by thisreference.

TECHNICAL FIELD

This invention relates generally to the surgical field, and morespecifically to a new and useful system and method for estimating theextracorporeal blood volume in a physical sample for use in surgicalpractice.

BACKGROUND

Overestimation and underestimation of patient blood loss is asignificant contributor to high operating and surgical costs forhospitals, clinics and other medical facilities. Specifically,overestimation of patient blood loss results in wasted transfusion-gradeblood and higher operating costs for medical institutions and can leadto blood shortages. Underestimation of patient blood loss is a keycontributor of delayed resuscitation and transfusion in the event ofhemorrhage and has been associated with billions of dollars in avoidablepatient infections, re-hospitalizations, and lawsuits annually. Thus,there is a need in the surgical field for a new and useful system andmethod for estimating extracorporeal blood volume in a physical sample.This invention provides such a new and useful system and method.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A is a flowchart representation of a method of a first preferredembodiment;

FIG. 1B is a flowchart representation of one variation of the firstpreferred method;

FIG. 2A is a flowchart representation of a method of a second preferredembodiment;

FIG. 2B is a flowchart representation of one variation of the secondpreferred method;

FIG. 3A is a flowchart representation of a method of a third preferredembodiment;

FIG. 3B is a flowchart representation of one variation of the thirdpreferred method;

FIG. 4 is a flowchart representation of one variation of the firstpreferred method;

FIG. 5 is a flowchart representation of one variation of the firstpreferred method;

FIG. 6 is a flowchart representation of one variation of the firstpreferred method;

FIG. 7 is a flowchart representation of another variation of thepreferred method;

FIG. 8 is a schematic of a system of a preferred embodiment;

FIG. 9 is a schematic of a variation of the preferred system; and

FIG. 10 is a graphical representation of an output in accordance with asystem or a method of a preferred embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of preferred embodiments of the invention isnot intended to limit the invention to these preferred embodiments, butrather to enable any person skilled in the art to make and use thisinvention.

1. First Method

As shown in FIG. 1A, a method S100 of a preferred embodiment forestimating the extracorporeal blood volume in a portion of a physicalsample includes: extracting a feature from a portion of an image of thesample in Block S110; tagging the portion of the image of the samplewith a blood volume indicator according to the extracted feature inBlock S120; and estimating the extracorporeal blood volume in at leastthe portion of the physical sample, associated with the portion of theimage of the sample, according to the blood volume indicator in BlockS130.

As shown in FIG. 7, the first preferred method S100 preferably functionsto estimate the volume of blood in the physical sample by analyzing animage of the sample. The image of the sample is preferably a color frameof a live video feed, wherein at least a portion of the physical sampleis visible in the frame. However, the image can alternatively be astatic or still image, an infrared image, a field of view of an opticalsensor, a black and white image, a fingerprint of a field of view of anoptical sensor, a point cloud, or any other suitable type of image. Insituations in which the physical sample does not fit within a field ofview of the camera or optical sensor, the image can be a scan of thephysical sample. The image can be captured and then stored on a local orremote data storage device for subsequent processing, though the imagecan alternatively or in parallel be processed in real time, or in partsor segments to avoid storage of the full image. The first preferredmethod S100 preferably estimates an extracorporeal blood volume thatincludes blood external the body of a patient or subject. Additionallyor alternatively, the first preferred method S100 can estimate anextravascular blood volume that includes blood within the body of apatient or subject but external the vascular system of the patient.

The physical sample is preferably an absorbent surgical gauze sponge, asurgical dressing, or a surgical towel, though the sample can be anyother textile. Additionally or alternatively, the physical sample can bea piece of clothing, a ground, table, wall, or floor surface, anexternal skin surface, a surgical glove, a surgical implement, or anyother surface, material, substrate, or object. A surgeon, nurse,anesthesiologist, gynecologist, soldier, paramedic, or other user canuse a machine or device incorporating the first preferred method S100 toestimate blood volume in one or more physical samples to generate atotal estimated blood loss (EBL) of a patient, such as during a surgery,childbirth or any other medical or health-related event. Alternatively,a law enforcement officer, forensic investigator, or other user can usea machine or device implementing the first preferred method S100 toestimate extracorporeal blood volume at a crime scene or to assessvictim risk during a medical emergency.

The first preferred method S100 can additionally or alternativelyfunction to estimate the volume, mass, or quantity of anotherblood-related parameter or extracorporeal blood volume indicator in thephysical sample, such as hemoglobin or red blood cell mass or volume inthe physical sample. Such blood-related parameters can then be evaluatedagainst additional variables or features to calculate the volume ofblood, hemoglobin, red blood cells, white blood cells, plasma, etc. inthe physical sample. For example, an estimated or measured hematocrit(HCT) of the blood of a patient can be used to estimate blood volume inthe sample according to the formulas:

${HCT} = {\frac{RBC}{EBL} = \frac{RBC}{{RBC} + {PV}}}$ HGB = .35 × RBCwherein RBC (red blood cell content) is substantially correlated withhemoglobin volume, PV is plasma volume, and EBL is estimated blood loss(or volume of blood in the physical sample) and is a composite of RBCand PV. The first preferred method S100 can additionally oralternatively function to detect presence of blood in the sample,compute blood spread rate, compute blood loss rate, calculate bloodsurface area, estimate patient risk level (e.g., hypovolemic shock),and/or determine hemorrhage classification of the patient. However, thefirst preferred method S100 can provide any other functionality, analyzeany other image type or format, estimate any other blood-relatedparameter, and/or calculate blood volume in the physical sample in anyother way.

The first preferred method S100 is preferably implemented in a handheld(mobile) electronic device, such as an application (or ‘app’) executingon a digital music player, a smartphone, or a tablet computer, as shownin FIG. 9, wherein a camera integral with the electronic device capturesthe image of the sample, wherein a processor integral with theelectronic device performs Blocks S110, S120, and S130, and wherein adisplay integral with the electronic device performs Block S160, whichrecites displaying the estimated blood volume of the portion of thephysical sample, the whole of the physical sample, and/or a summed totalblood volume across multiple physical samples. In this implementation,the electronic device can alternatively communicate with a remoteserver, such as via a wireless communication module 150 implementingcellular, Wi-Fi, or Bluetooth protocol, wherein the server performs atleast some of Blocks S110, S120, and S130, and wherein at least some ofthe outputs of Blocks S110, S120, and S130 are transmitted back to theelectronic device and subsequently displayed. However, the firstpreferred method S100 can also be a standalone blood volume estimationsystem, such as a system including a staging tray configured to supporta sample, a camera configured to image the sample, and a processorconfigured to perform at least a portion of the first preferred methodS100 and/or a communication module that communicates with a remoteserver configured to perform at least a portion of the first preferredmethod S100. However, the first preferred method S100 can be implementedin any other system, device, or combination thereof.

The first preferred method S100 can therefore be useful in a hospitalsetting, such as in a surgical operating room, in a clinical setting,such as in a delivery room, in a military setting, such as on abattlefield, in a law enforcement setting, such as at a crime scene, orin a residential setting, such as to monitor blood loss due tomenorrhagia (heavy menstrual bleeding) or epistaxis (nosebleeds).However, the first preferred method S100 can be useful in any othersetting.

As shown in FIGS. 1, 2, and 3, Block S110 of the first preferred methodS100 includes extracting a feature from a portion of an image of thesample. The extracted feature of the portion of the image preferablyenables correlation (or pairing) of the portion of the image with ablood loss indicator of the portion of the sample in Block S120, whichcan further enable estimation of the blood volume in the portion of thesample in Block S130. The extracted feature is preferably an intensity,luminosity, hue, saturation, brightness, gloss, or other color-relatedvalue of the portion of the image in at least one component space, suchas the red, blue, green, cyan, magenta, yellow, key, and/or Labcomponent spaces. Furthermore, the extracted feature can be a histogramof various color values across a set of pixels in the portion of theimage. Additionally or alternatively, the extracted feature can be anestimated surface area of the sample shown in the image, an estimatedsurface area of a bloodied portion of the sample, a pixel count of theportion of the sample, a pixel count of the entire sample, or a pixelcount of only the bloodied region of the sample, a color intensity valueof an unsoiled portion of the sample, or any other relevant featureinherent in or available for extraction from the portion of the image ofthe sample. Furthermore, Block S110 can include extracting any number offeatures from all or a portion of the image of the sample.

Block S110 can similarly include accessing non-image features, such as acurrent patient intravascular hematocrit, an estimated patientintravascular hematocrit, an historic patient intravascular hematocrit,a weight of the sample, a clinician-estimated sample blood volume,computer-vision-based or gravimetric or human-generated estimates ofblood volumes of previous samples, an ambient lighting condition, a typeor other identifier of the physical sample, properties of the physicalsample, a patient vital sign, patient medical history, an identity of asurgeon, or a type of surgery. Any of these non-image features caninform selection of template images for comparison with the portion ofthe sample image, selection of a particular parametric model orfunction, definition of alarm triggers for misplaced surgical gauzesponges, definition of alarm triggers for excess fluid or blood loss,transformation of extracted features into the blood volume indicator,and/or estimation of blood volume from the blood volume indicator.However, any of these non-image features can modify enable, or informany other function of the first preferred method S100.

As shown in FIG. 4, Block S110 preferably includes segmenting the image,including isolating a first segment of the sample image representativeof the physical sample that is a bloodied object (e.g., a surgical gauzesponge). Block S110 preferably subsequently further segments the firstregion to define the portion of the sample image that corresponds to aparticular portion of the physical sample captured in the sample image.Segmenting the sample image into multiple image segments preferablyincreases the resolution and/or accuracy of the estimated blood volumeof each portion of the physical sample. The size and shape of each imagesegment can be static, wherein each segment comprises a predefinednumber of pixels in the image and/or a predefined dimension in physicalspace, as shown in FIG. 9. For example, the image segment can define aten-pixel by ten-pixel rectilinear area of the image or afive-millimeter equilateral triangular area of the physical sample. Inanother variation, the image segment can be isolated according toproperties of individual pixels or groups of pixels in the image, suchas hue, saturation, shade, brightness, chroma, wavelength, or any othermetric of color or light, as shown in FIG. 7. In this alternative, thesample image can be dynamically segmented, wherein portions of thesample image are separated by color property or other features ratherthan by (or in addition to) pixel location or by location on thephysical sample. However, the portion of the sample image can includethe whole of the physical sample associated with the sample image, orthe sample image can be segmented or apportioned according to any otherschema. The portion of the sample image is preferably a single segmentor region including multiple pixels of the image, but the portion of thesample image can alternatively be a plurality of image segments orregions of the sample image, can be of any other size, and/or can be ofany other form.

In a variation of the first preferred method S100, Block S110 extracts afeature from the sample image that is a dimension of the physicalsample. In one example implementation, Block S110 implements objectrecognition to isolate an object of known type within the field of viewof the optical sensor and/or within the sample image. The object can bea surgical tool, a surgical tray, an operating table, a surgical gauzesponge, a suction canister, or any other object of known dimension. Fromthis known dimension, a dimension of the physical sample can beextrapolated, such as by estimating the distance from and/or anglebetween the optical sensor and the known object and comparing theposition of the sample and the known object in the image. In anotherexample implementation, Block S110 analyzes shadows in the sample image,coupled with known locations of light sources, to estimate an angle anddistance between the physical sample and the capture origin (i.e. thelocation of the camera or optical sensor when the sample image wascaptured). In yet another example implementation, the optical sensor isarranged at a known distance from and angle to a staging tray on whichthe physical sample is arranged for imaging, and Block S110 includesextrapolating the dimension of the physical sample or a portiontherefore based upon known placement of the optical sensor relative thestaging tray. A further example implementation, Block S110 manipulatesan IR, sonic, laser, or other type of distance sensor arranged adjacentthe optical sensor to transmit a signal toward the physical sample todetermine the distance and/or angle between the physical sample and thecapture origin of the image. However, a dimension of the physical sampleor a portion thereof can be estimated or determined in any other way.

In the foregoing example implementations, the distance and/or anglebetween the sample and the optical sensor can be automatically extractedfrom the image to inform a transform from pixel count of the portion ofthe sample image into a physical dimension (e.g., inch, centimeter) ofthe corresponding portion of the physical sample in Block S110. Theestimated angle and/or distance can therefore define an extractedfeature of the sample image that informs the generation of the bloodindicator tag and/or the transformation of the blood indicator tag intothe estimated blood volume in the portion of the physical sample.However, the distance and/or angle value(s) can be input by a user(e.g., a surgeon, a nurse), extrapolated from data generated by anon-optical sensor, or calculated or gathered in any other way to definea non-image feature related to the sample.

Block S110 can additionally or alternatively implement any objectlocalization, segmentation (e.g. using edge detection, backgroundsubtraction, graph-cut-based algorithms, etc.), gauging, clustering,pattern recognition, template matching (using any one of variousmetrics), feature extraction, descriptor extraction (e.g. extraction oftexton maps, color histograms, HOG, SIFT, etc.), feature dimensionalityreduction (e.g. PCA, K-Means, linear discriminant analysis, etc.),feature selection, thresholding, positioning, color analysis,parameteric regression, non-parametric regression, unsupervised orsemisupervised parametric or non-parametric regression, or any othertype of machine learning or machine vision to estimate a physicaldimension of the sample. Such methods preferably compensate for varyinglighting conditions of the physical sample, warping of the physicalsample (e.g., a wrinkle or warped gauze sponge), warping of the image ofthe physical sample (e.g., due to optical distortion caused by a lens ofthe optical sensor), variations in composition of the fluid present inor on the sample, or any other inconsistency or variable prevalent inany use scenarios. For example, once the object, materials, gauze type,etc. of the physical sample is identified, the estimated surface area ofthe physical sample can be compared with a known surface area of atemplate sample of the same object, material, or gauze type to correctfor area estimation errors, such as due to a wrinkle or othernon-uniformity in the physical sample when the sample image was taken.

In another variation of the first preferred method S100 and as shown inFIG. 4, Block S110 includes assessing ambient lighting conditions,wherein the ambient lighting conditions define an extracted feature. Forexample, the ‘redness,’ ‘greenness,’ and ‘blueness’ values (i.e. colorvalues in the red, green, and blue color component spaces) of pixels ofbloodied regions in the sample image can be combined into a weightedcomposite color value according to ambient lighting conditions proximalthe sample when the sample image was captured. In this example, theweighted composite color value can then be fed into a parametricfunction or compared against color values of template images to generatethe blood volume indicator.

In one variation of the first preferred method S100 shown in FIG. 4,Block S110 includes extracting a feature that identifies the physicalsample in the sample image as an absorbent surgical gauze sponge. Inthis variation, Block S110 preferably implements machine vision, such asobject segmentation, edge detection, pattern recognition, or templatematching, to determine if the sample image includes an absorbentsurgical gauze sponge or if an absorbent surgical gauze sponge is withina field of view of a camera or other optical sensor. Furthermore, BlockS110 can preferably determine the type of absorbent surgical gauzesponge, such as laparotomy or RAY-TEC gauze, which can inform selectionof a template image of a similar template sample type for comparisonwith the portion of the sample. Block S110 can additionally oralternatively identify thread count, external dimension, color, physicaltag, or any other identifying feature or property of the physical sampleto identify the type of sample or a fluid absorptivity, saturationvolume, dry weight or mass, dry color, or any other property of thephysical sample. Block S110 can therefore reduce processing timenecessary to return a template image match for the portion of the sampleby isolating key identifying features of the physical sample. Similarly,Block S110 can improve accuracy of the blood volume estimation byisolating key properties that affect fluid absorbance and a correlationbetween fluid volume and optical properties of the physical sample.

Block S110 can additionally or alternatively extract features from thesample image that identify other relevant objects, materials, or fluidsin the sample image and/or the field of view of the optical sensor. Forexample, Block S110 can recognize drops, pools, or smears of blood on asurgical tool, tray, table, wall, floor, or other surface as containingblood. Block S110 can initiate an estimation of blood volume in or on asample that is other than an absorbent surgical gauze sponge, surgicaldressing, or surgical towel. In this variation, template matching can beused to estimate blood volume in or on the physical sample, as describedbelow, although color value, translucency, saturation, dimension, or anyother metric of the sample can be used to parametrically ornon-parametrically generate the blood volume indicator tag and/orestimate the extracorporeal blood volume in at least the portion of thephysical sample.

As shown in FIGS. 1, 2, and 4, Block S120 of the first preferred methodS100 includes tagging the portion of the image of the sample with theblood volume indicator according to the extracted feature. Theextracorporeal blood volume indicator tag is preferably an intermediateparameter for a region of interest in the sample image that translatespixel-level data in the sample image into a blood volume-relatedvariable. The blood volume indicator tag is therefore preferably anestimate of hemoglobin content (e.g., mass, volume, density, percentageby weight, etc.) in the portion of the sample, though the extracorporealblood volume indicator tag can alternatively be an estimate of red bloodcell count or content, white blood count or content, platelet count orcontent, plasma content, or any other suitable extracorporeal bloodvolume indicator. The tag can also include any other relevantinformation, such as estimated hematocrit of the blood in the portion ofthe physical sample, a time stamp of when the sample image was taken, atime stamp of when the sample image was analyzed, or volume orconcentration of other fluids present on or in the portion of thesample, such as bile, saliva, gastric fluid, mucus, pleural fluid,saline, or fecal matter. Generally, the blood volume tag is preferablyof a form that can be transformed or manipulated into an estimatedextracorporeal blood volume in all or a portion of the sample.Furthermore, the extracorporeal blood volume indicator tag for theportion of the sample image is preferably stored with the portion of thesample image or as a pointer to the portion of the sample image.

In one variation of the first preferred method S100, Block S120 includescomparing the extracted feature of the portion of the image of thesample against similar features extracted from template samples (e.g., atraining set, samples analyzed previously) of known blood volumeindicators and/or known extracorporeal blood volumes. In this variation,the portion of the image is tagged with the blood volume indicator basedupon a non-parametric correlation with one or more template samples. Forexample, in this variation of the first preferred method S100, BlockS120 can include implementing a K-nearest neighbor method to compare theextracted feature of the image that is a redness intensity in the redcomponent space with redness intensity values of template samples. Inthis example, Block S120 can further include implementing a K-nearestneighbor method to compare extracted features that include a greennessintensity and a blueness intensity (in conjunction with a rednessintensity) of pixels from bloodied regions in the sample image withgreenness and blueness intensity values of template samples.

In one example implementation of this variation of the first preferredmethod S100, Block S120 includes pairing the portion of the image of thesample to a template image of known extracorporeal blood volumeindicator. Each template image is preferably contained within a libraryof template images, and each template image is preferably an image of atemplate sample of known blood, hemoglobin, red blood cell mass orvolume (e.g., per unit physical area), and/or any other suitableblood-related parameter, blood volume indicator, or feature. Eachtemplate image in the library is preferably tagged with anextracorporeal blood volume indicator such that the portion of thesample image can be matched to a template image in Block S110, and suchthat a tag, indicative of the blood volume in the portion of thephysical sample, can be associated with the portion of the sample imagein Block S120.

The library of template images can be assembled in a variety of ways. Inone example, an image is taken of a template sample that is a usedsurgical gauze, blood is washed from the used gauze and assayed todetermine the hemoglobin mass absorbed into the used gauze, the image ofthe template sample is tagged with the hemoglobin mass (theextracorporeal blood volume indicator), and the image is catalogued inthe library. In another example, a template sample is prepared by addinga known volume of blood (of known hematocrit) to a surgical gauze of aknown size, an image of the template sample is taken, the image of thetemplate sample is tagged with the known blood volume (theextracorporeal blood volume indicator), and the image is catalogued inthe library. The blood volume tag of each image template is preferably avolume or mass of a blood-related parameter, such as hemoglobin or redblood cell content per physical area (e.g., 1 cm²) such that, in BlockS130, a blood volume indicator tag of a portion of the image can bemultiple by an estimate physical area (or volume) of the correspondingportion of the physical sample to estimate the extracorporeal bloodvolume in the portion of the sample, as shown in FIG. 7. However, thetemplate sample for each template image can be prepared in any other wayor combination of ways, and the extracorporeal blood volume indicatorcan be any other suitable parameter or metric. The library preferablycontains a large number of template images to account for variance inlighting, image quality, type of physical sample (e.g., type of surgicalgauze sponge), volumes, concentrations, or hematocrits of blood or otherindicator in each sample, “age” of the physical sample, surgicalconditions, or any other suitable variable. Furthermore, the templateimages in the library can also be grouped, such as according to: thetype of template sample, such as a gauze sponge, floor, operating table,clothing; lighting or backlighting of the template sample; hematocrit ofblood in a template sample; thread count of the template sample that isa textile; quality of the image of the template sample, such as depth offield, focus, distance between the template sample and an opticalsensor; or any other suitable parameter. The library can be storedlocally on a machine or system configured to perform at least a portionof the first preferred method S100, or remotely, such as on a remoteserver or hard drive accessible by the machine or system when performingat least a portion of the first preferred method S100.

In this example implementation, the sample image can be compareddirectly to the template image via template matching in Block S120. InBlock S110, each image segment can be decomposed into features that areseparate color components (e.g., red, green, and blue), and the absolutedifference in pixel intensity for the pixels in the portion of thesample image and the pixels in the template image can be calculated forat least one color component. (However, the sample image canalternatively be decomposed prior to segmentation.) In this exampleimplementation, the absolute difference in pixel intensity is preferablycalculated at a wavelength of light that correlates with theextracorporeal blood volume indicator. For example, the absolutedifference in pixel intensity for the portion of the sample image andthe template image can be calculated at 400 nm, a wavelength that cancorrelate well with hemoglobin concentration for certain absorbentsurgical gauze sponges. The template image is preferably paired with theportion of the image when a substantially minimal sum of absolutedifference in pixel intensity between the portion of the sample imageand the template image is calculated.

Alternatively, Block S120 can implement a texton map to pair the sampleimage with one or more template images. In this implementation, to buildthe template image library patches from template (training) images canbe clustered into centroid patches, such as by k-means clustering. Foreach pixel or set of pixels in each training image, the index of thecentroid patch nearest the patch surrounding the pixel can be calculatedsuch that a histogram, of the nearest-centroid indices within a windowaround each pixel, can be constructed. By averaging the histograms ofall background pixels, a background histogram centroid can also beconstructed. Clean and bloodied histogram centroids for physical samples(e.g., surgical gauze sponges) can be similarly constructed.Alternatively, a classification algorithm such as SVM, Naïve Bayes, LDA,K-Nearest-Neighbors, or logistic regression, can be trained usinghistograms centered around or mostly containing background, bloodied,and unsoiled pixels. When the portion of the sample image is comparedwith template images in the template image library, histogram of thenearest-patch-centroid indices around each pixel in the portion of thesample image is generated and classified based upon a comparison of thehistogram and histogram centroid of the pixel, or based upon the outputof one of the learned classifiers described above. The histograms and/orhistogram centroids of the pixels in the portion of the sample image canthen be compared with a subset of histograms and/or histogram centroidsof pixels of the template images, based upon the determined class ofphysical sample, to pair one or more template images with the sampleimage.

In this example implementation, Block S120 therefore preferably recitesstepping through subsequent template images in the template imagelibrary until a suitable match is found for the portion of the sampleimage. However, the hue, saturation, shade, brightness, chroma,intensity of wavelength, wavelength range, histogram, histogramcentroid, class, or any other color property (e.g., feature) of theportion of the sample image and the template image can be compared inBlock S120. In this example implementation, the portion of the sampleimage and the template image are preferably compared substantiallydirectly. However, the template image and the portion of the sampleimage can be compared via template matching incorporating any othervision algorithm or image processing method.

In another example implementation of this variation of the firstpreferred method S100, each template image is a different color or huein a library of color palettes, wherein each color correlates with adifferent blood volume or blood volume indicator. In this exampleimplementation, the library preferably includes color palettes fordifferent types of surgical sponge gauzes, surgical towels, surgicaltool surfaces, floor surfaces, operating or delivery table surfaces,and/or any other common surface, material, object, or feature, whereineach color that is a template image in a color palette is associatedwith a particular red blood cell content or indicator for a particulartype of physical sample. In this example implementation, the templateimage that is a color can be an image of the color or a numerical coloridentifier, such as a HEX code value (e.g., #FF0000, #A00000, #880000,etc.) or an RGB code value (e.g., (255, 0, 0), (160, 0, 0), (190, 0, 0),etc.).

In yet another example implementation of this variation of the firstpreferred method S100, the feature extracted from the portion of thesample image in Block S110 is a redness value, wherein the redness valueis an intensity of a wavelength or composite intensity of a range ofwavelengths of light, redness hue, redness saturation, or any othersuitable light- or color-related value. Block S110 can similarly extractgreenness, blueness, or other color component values of one or morebloodied pixels in the sample image. Generally, Block S110 preferablydecomposes the sample image into distinct color spaces, such as red,green, and blue component spaces, wherein a color value or intensity iscalculated for the portion of the sample image in each color space.Furthermore, the portion of the sample image that is decomposed in BlockS110 preferably includes red pixels indicative of blood content in theportion of the physical sample that is associated with the portion ofthe sample image. In Block S120, the color value(s) of the portion ofthe image are then compared substantially directly with color values oftemplate images until a suitable match is found.

In this variation of the first preferred method S100, template imageswith properties substantially dissimilar from those of the portion ofthe physical sample or the sample image can be withdrawn from comparisonwith the portion of the sample image in Block S120 in order to reduceprocessing time required to find a template image match. In one exampleimplementation, template images of template samples of surfaces,products, materials, or dimensions substantially dissimilar from that ofthe portion of the physical sample are excluded from comparison. Forexample, Block S110 can extract a thread count feature from the sampleimage, wherein the thread count feature identifies the physical sampleas laparotomy gauze, and wherein all template images of template samplesthat are not of laparotomy gauzes are removed from comparison with theportion of the sample image. In another variation, thresholding is usedto remove substantially irrelevant template images from the test pool.In one example, template images with redness values (e.g., intensity,hue, saturation, shade, brightness, chroma, wavelength range)substantially dissimilar from that of the portion of the sample imageare excluded from comparison. Tree search can additionally oralternatively be used to reduce processing time. However, templateimages can be grouped in the template library and selected or deselectedfor comparison with the portion of the sample image according to anyother schema.

In another variation of the first preferred method S100, Block S120includes transforming the extracted feature of the portion of the imageof the sample into the blood volume indicator. In this variation of thefirst preferred method S100, Block S120 preferably implements analgorithm or other mathematical transformation to convert the extractedfeature into the blood volume indicator for the portion of the image ofthe sample. Therefore, in this variation, Block S120 preferablyimplements parameterized generation of the blood volume indicator.

In one example implementation, color values of the template images areused to generate a mathematical function, curve, or algorithm thatcorrelates the extracted feature to the blood volume indicator.Generally, the extracted feature of the portion of the sample image(e.g., redness intensity in the red component space, blueness intensityin the blue component space, greenness intensity in the green componentspace, or a composite of two or three color intensities) can be pluggedinto a parametric function (e.g., intensity-blood volume function) todirectly calculate the blood volume indicator, from the extractedfeature, for the portion of the sample image. For example, reflectanceof oxygenated hemoglobin (Hb_(O2)) can be correlated with certainwavelengths of light to substantially directly estimate the content ofhemoglobin in the portion of the physical sample associated with theportion of the image. In this example, because the hemoglobin content ofa wet (hydrated) red blood cell is typically about 35%, red blood cellcount can be extrapolated from hemoglobin content.

Blocks S120 and S130 can implement both parametric and non-parametrictechniques or methods to correlate one of more extracted features to oneor more blood volume indicators. For example, extracted features thatare color values in the red, green, and blue color spaces can becompared with template images via non-parametric techniques (e.g.,template matching) to tag the portion of the sample with the bloodvolume indicator, and an extracted feature that is an estimated surfacearea of a bloodied region of the physical sample can be transformedaccording to a parametric function to generate a coefficient forconversion of the blood volume indicator into an estimated blood volumein the portion of the sample. In this example, another extracted featurethat is the type of physical sample (e.g., laparotomy gauze, RAY-TECgauze, surgical table, floor, article of clothing) functions to qualifythe sample to inform selection of template images for comparison withthe portion of the sample image. However, Block S120 and S130 canmanipulate any relevant image-based feature extracted in Block S110 orany non-image-based feature (e.g., sourced from a clinician, sourcedfrom a medical record, etc.) to generate the blood volume indicator ofthe portion of the image and the estimated blood volume for at least theportion of the sample, respectively.

As shown in FIGS. 1 and 5, Block S130 of the first preferred method S100includes estimating the extracorporeal blood volume in at least aportion of the physical sample, associated with the portion of thesample image, according to the blood volume indicator tag. For exampleand as described above, the blood volume indicator tag that is ahemoglobin content can estimate red blood cell volume through theformulasRBC=HGB/0.35 orHCT=3×HGB,which in turn can be used to predict blood volume. The blood volume foreach portion of the physical sample correlating with a portion of theimage can be independently calculated and then summed to estimate atotal blood volume in the physical sample. Alternatively, the bloodvolume indicator tags for substantially all portions of the image can besummed and/or averaged and the total blood volume in the physical samplecalculated at once. The estimated blood volumes across multiple samplescan then be summed to generate a total blood volume in the samples,which preferably correlates with a total estimated blood loss of apatient. However, Block S130 can additionally or alternatively includeestimating total hemoglobin mass or volume, total red blood cell mass orvolume, or any other blood-related metric in the physical sample oracross multiple samples.

As shown in FIG. 1B, a variation of the first preferred method S100further includes Block S140, which recites identifying the physicalsample in the image as a type of absorbent gauze sponge. Block S140preferably implements machine vision techniques to determine the type ofphysical sample, as described above. From identification of the type ofphysical sample in Block S140, the first preferred method S100 canaccess sample-specific data such as dry weight, absorptivity, fluidsaturation volume, or any other data or property of the physical sample,which can enable extraction of additional blood-related data from theimage of the physical sample.

As shown in FIG. 1B, another variation of the first preferred methodS100 can further include Block S150, which recites indexing a samplecount for the physical sample. The sample count is preferably a count ofabsorbent surgical gauze sponges, dressings, or towels, though thesample count can additionally or alternatively be a count of blooddroplets, blood drops, pools of blood, bloodied articles of clothing,bloodied surgical tools, or any other relevant or suitable bloodformation or bloodied object. The sample count is preferably displayedwith the estimated blood volume of the portion of the physical sample,and the sample count is preferably indexed substantially in real timewhen the image of the physical sample is taken. However, Block S150 canfunction in any other way to index the sample count and to provide thisinformation to a user.

As shown in FIG. 1B, another variation of the first preferred methodS100 further includes Block S160, which recites displaying the estimatedblood volume in the portion of the physical sample, the estimated bloodvolume in the whole physical sample, and/or the estimated total bloodvolume across multiple physical samples. At least some of this data ispreferably presented to a user, such as to a surgeon, a nurse, ananesthesiologist, a gynecologist, a doctor, or a soldier. This data ispreferably rendered on a digital display of a machine or systemconfigured to perform at least a portion of the first preferred methodS100. As shown in FIG. 9, this data can be presented in the form of anaugmented reality overlay on top of the static sample image depicted onthe display. Alternatively, this data can be presented in the form of adynamic augmented reality overlay on top of a live video stream capturedby the optical sensor and depicted on the display. For example, data canbe presented in an augmented reality overlay on subsequent scannedimages of one physical sample, wherein the camera captures digitalimages at a rate such as 30 frames per second and the augmented realityoverlay updates with each new frame or number of frames. This data canalternatively be presented in table, chart, or diagram including theestimated blood volume in one or more physical samples over a period oftime. Other blood-related metrics can also be estimated or maintained inthe first preferred method S100 and presented in Block S160, such asblood spread rate, blood surface area, patient risk level, or patienthemorrhage classification. However, this data or any other blood-relatedmetric or patient information can be presented in any other way or formin Block S160.

As shown in FIG. 1B, another variation of the first preferred methodS100 further includes Block S170, which recites estimating patient bloodloss by summing the blood volume estimate of the physical sample withprevious blood volume estimates of other physical samples. Additionallyor alternatively, the blood volume estimate of the physical sample canbe stored for future summation with blood volume estimates of additionalphysical samples. By summing blood volume estimates across multiplephysical samples, blood loss of a patient can be tracked over time. Forexample, during a surgery, used surgical gauze sponges can be analyzedvia Blocks S110 and S120, wherein a running summation of blood volumesin each used gauze sponge provides time-elapse estimates of total bloodloss of the patient, as shown in FIG. 10. This may be useful inestimating patient risk, in determining when to administer saline orprovide a blood transfusion, in maintaining record of surgical events,and/or in estimating future blood-related events or patient needs. Otherblood-related metrics can also be estimated or maintained in Block S130and summed over time in Block S170.

As shown in FIG. 1B, another variation of the first preferred methodS100 further includes Block S180, which recites comparing the identifiedphysical sample against a set of past identified physical samples. Inthis variation, Block S150 preferably indexes the sample counter onlywhen the identified physical sample is determined to be unique amongstthe set of past identified physical samples. Block S180 thereforefunctions to determine if a previous sample image including the samephysical sample was already analyzed according to any of Blocks S110,S120, and/or S130. Block S180 preferably substantially guards againstdouble counting of the estimated blood volume in the physical sample inBlock S170. Each sample image, a fingerprint of each sample image, or afingerprint of each physical sample is therefore preferably stored, suchas on a local or remote sample image database, such that subsequentsample images or physical samples can be compared against past sampleimages or physical samples in Block S180. In Block S180, comparison ofthe sample image with previous sample images can require scale,rotation, mirror, stretch or other transformations or fingerprinting ofthe sample image and/or or previous sample images. Edge detection,segmentation, pattern recognition, feature extraction, and/or othermachine vision techniques can be used to determine the uniqueness ofbloodied regions of the physical sample shown in the sample image,relative to bloodied regions of other, previously analyzed physicalsamples. However, Block S180 can function in any other way to identifythe sample image as including the physical sample that was included in aprevious sample image.

As shown in FIG. 1B, another variation of the first preferred methodS100 further includes Block S190, which recites updating a digitalmedical record of the patient with the estimated blood volume in thephysical sample. Block S190 can additionally or alternatively update themedical record of the patient with the estimated blood volume acrossmultiple physical samples, the estimated blood loss of the patient,patient blood loss trends, or any other relevant metric or datagenerated related to the circulatory system of a patient. The digitalmedical record can be maintained locally on a machine or systemimplementing the first preferred method S100 or on a local network orremote server accessed by the machine or system to retrieve, update,and/or upload the digital medical record.

In a further variation of the first preferred method S100, the physicalsample is a fluid canister that collects bodily fluids of a patient,such as blood, bile, saliva, gastric fluid, mucus, pleural fluid, urine,or fecal matter, wherein the image is an image of the fluid canister. Inan example implementation of this variation, Block S110 can includeextracting features that include a volume of fluid within the canister,as well as redness, greenness, and blueness intensities of the portionof the image of that canister that includes bloodied pixels andpreferably includes little to no glare. Furthermore, Block S120 caninclude estimating a percentage of blood within the canister relative toother bodily fluids based upon the extracted color values, and BlockS130 can include estimating the volume of blood within the canister. Inthis variation of the first preferred method S100, the optical sensorthat captures the image of the fluid canister is preferably mounted tothe fluid canister. In one example implementation, the optical sensor ismounted to the side of and facing the fluid canister that is cylindricalsuch that the fluid level in the fluid canister can be estimateddirectly from the sample image. In another example implementation, theoptical sensor is mounted overhead the fluid canister that also includesa fluid level sensor, wherein an output of the fluid sensor defines anon-image feature that informs at least one of the blood volumeindicator and the estimated blood volume in the fluid canister.Alternatively, the optical sensor can be incorporated into a handhelddevice, wherein a user scans the fluid canister with the optical sensorto capture the sample image. In addition, an auxiliary light source(such as a lamp or laser next to the canister) could be added to thesystem to enhance the correlation of color with concentration ofhemoglobin or other substances. Alternatively or in addition, ambientlight could be assessed and used as a feature.

Because fluid is added to the fluid canister over time, subsequentsample images of the fluid canister can be captured and analyzed overtime, via the first preferred method S100, to generate a time-dependent,historical chronicle of fluid content of the fluid canister. Estimatedblood volume in the fluid canister can therefore be monitored over time,such as to generate a trend in blood loss for a patient. Such data canbe useful to trigger alarms if patient blood loss is occurring toorapidly or if patient blood loss has reached a critical total volume orcritical red blood cell loss. However, loss of other fluids can also bemonitored. For example, urine content (or total water content) of thefluid canister can enable tracking of patient hydration level such thatthe patient can be administered saline when hydration level or hydrationloss surpasses a threshold. Differences between fluid color propertiesof one sample image at a first time and a subsequent sample image at asecond time can indicate concentration changes of fluids in the fluidcanister between the first and second times. Furthermore, a change influid level in the canister between the first and second times, coupledwith fluid concentration changes, can indicate the floor rate of fluidsinto (or out of) the fluid canister. Estimated blood and/or other fluidloss through analysis of the sample image of the fluid canister can befurther fed into analyses of sample images of surgical sponge gauzes,implements, surfaces, etc. to map total blood and/or other fluid loss ofthe patient over time. However, the first preferred method S100 canfunction in any other way to estimate the volume of blood within thephysical sample that is a fluid canister.

One variation of the first preferred method S100 further comprisesestimating the volume of extracorporeal non-blood fluids in the physicalsample, such as ascites, saline irrigant, bile, plasma, urine, orsaliva. In one example implementation, the redness of the physicalsample (e.g., color intensity of image pixels associated with thephysical sample in the red component space) is correlated with a totalred blood cell count or volume in the physical sample, wherein the totalred blood cell count or volume is subtracted from the estimated totalextracorporeal blood volume in the sample, according to an estimated ormeasured hematocrit of the blood in the physical sample, to estimate thetotal volume of plasma in the physical sample. In another exampleimplementation, the estimated total extracorporeal blood volume isconverted to as estimated total extracorporeal blood weight (or mass),wherein the estimated total extracorporeal blood weight (or mass) anddry weight (or mass) of the physical sample are subtracted from a wetweight (or mass) of the physical sample to estimate the total weight (ormass of volume) of substantially clear fluids (e.g., saline, intestinalascites) in the physical sample. In this example implementation, thefirst preferred method S100 preferably accessed a mass or weightmeasurement of the physical sample through a scale electrically coupledto the machine or device implementing the first preferred method S100.Furthermore, the first preferred method S100 preferably implementsmachine vision techniques to determine the type of physical sample, suchas a surgical dressing, a surgical gauze sponge, or a surgical towelfrom a particular manufacturer. The first preferred method S100 can thenaccess sample-specific data such as dry weight, absorptivity, fluidand/or saturation volume to enable extraction of further data related toblood or non-blood fluids in the physical sample. However, the firstpreferred method S100 can implement any other technique or method toestimate the volume, weight, or mass of an extracorporeal non-bloodfluid in the physical sample.

However, the first preferred method can additionally or alternativelyanalyze one or more extracted and/or non-image features to estimate anyone or more of hemoglobin mass, hematocrit, hemoglobin concentration,fresh frozen plasma, packed red blood cells, colloids, platelets,crystalloid, or any other blood-related parameter of the patient. Anyone or more of these blood-related parameters can additionally oralternatively be rendered on a display of the machine, system, or deviceimplementing the first preferred method S100.

One variation of the first preferred method includes recognizinggestures of a user to control operation of the machine, system, ordevice implementing the first preferred method S100. In this variation,the preferred method preferably accesses a live video feed captured bythe optical sensor that records the image of the physical sample or byany other optical sensor or camera coupled to the machine, system, ordevice implementing the first preferred method S100. Because the firstpreferred method is preferably implemented during a surgery or othermedical event or emergency during which a user is likely wearing aglove, the first preferred method S100 is preferably controlled vianon-contact means. Generally, this variation of the first preferredmethod S100 preferably recognizes non-contact hand gestures. In oneexample, a ‘thumbs up’ can indicate that the user accepts the detectionof the physical sample and the extracorporeal blood volume estimation ofthe physical sample. The extracorporeal blood volume can then be addedto an aggregate extracorporeal blood volume estimated for a set ofphysical samples. Similarly, a ‘thumbs down’ can reject the detectionand extracorporeal blood volume estimation for the physical sample. Inanother example implementation, a user can scroll through availablephysical sample types by sweeping a hand to the left or right.Similarly, the user can scroll through images of previous samples bysweeping a hand vertically. However, any other gesture can be recognizedin any other way to control any other function of the first preferredmethod S100.

Another variation of the first preferred method S100 further functionsto generate alarms or warnings related to the circulatory system of apatient. In one example, the preferred method S100 generates a warningthat a physical sample that is a surgical sponge gauze was lost or leftinside the patient if not identified within a threshold time (e.g., onehour) after being checked into a surgery. In another example, the firstpreferred method S100 sounds an alarm when the total estimated blood orred blood cell loss of the patient surpasses a threshold level. In thisexample, the threshold blood or red blood cell volume can be unique tothe patient and based upon any one or more of the age, gender, weight,medical history, etc. of the patient. In another example, the firstpreferred method S100 issues a warning of trends in patient blood loss,such as based upon blood distribution across multiple physical samples(e.g., sponges) over time. However, the first preferred method canadditionally or alternatively provide data and/or warnings relating to arate of blood loss, a rate of blood loss relative to sponge count, arate of sponge usage, a histogram of sponge usage, or any other suitabledata or warning related to the circulatory system of the patient.

2. Second Method

As shown in FIG. 2, a second preferred method S200 for estimating theextracorporeal blood volume in a portion of a physical sample, includes:comparing a portion of an image of the sample with a template image ofknown extracorporeal blood volume indicator in Block S210; tagging theportion of the image of the sample with a blood volume indicatoraccording to the template image that is matched to the portion of theimage of the sample in Block S220; and estimating the extracorporealblood volume in at least a portion of the physical sample, associatedwith the portion of the image of the sample, according to the bloodvolume indicator in Block S230.

The second preferred method S200 preferably implements non-parametricestimation (e.g., template matching) of extracorporeal blood volume inthe physical sample, as described above. Generally, Block S220preferably incorporates a variation of Block S220 of the first preferredmethod S100, and Block S230 preferably incorporates a variation of BlockS130 of the first preferred method S100. However, as shown in FIG. 3B,the second preferred method S200 can implement any other technique,method, implementation, and/or variation of the first preferred methodS100 described above.

One variation of the second preferred method S200 includes accessing thetemplate image that is a color model paired with a blood volumeindicator. The color model can be a template image, a representation ofor feature extracted from a template image, a mathematical function oralgorithm, or any other suitable color model correlating an extractedfeature of the sample image with a blood volume indicator. In thisvariation, Block S210 can include comparing the portion of the image ofthe sample with the template image to generate the blood volumeindicator tag that is a composite of the known blood volume indicatorsof the multiple color models, such as a first and a second templateimage that each include a color model paired with a blood volumeindicator.

Block S220 of the second preferred method S200 can include tagging theportion of the image of the sample with the blood volume indicator thatis an estimated hemoglobin mass. Furthermore, Block S230 of the secondpreferred method S200 can include estimating the extracorporeal bloodvolume in at least the portion of the physical sample according to thehemoglobin mass and an estimated hematocrit of blood in the physicalsample. However, Blocks S220 and S230 of the second preferred methodS200 can function in any other way, and the second preferred method canimplement any other Block, variation, example, or implementation of thefirst preferred method S100.

3. Third Method

As shown in FIG. 3A, a third preferred method S300 for counting physicalsurgical samples includes: identifying a physical sample in a field ofview of an optical sensor in Block S310; indexing a sample counter forthe identified physical sample in Block S320; extracting a feature froma portion of the field of the view of the optical sensor in Block S320;and estimating the extracorporeal blood volume in a portion of thephysical sample based upon the extracted feature in Block S340.

The third preferred method S300 preferably functions to identify aphysical sample, update a sample count, and estimate the volume of bloodin the physical sample by analyzing the field of view of the opticalsensor that includes the physical sample. The field of view of theoptical sensor is preferably captured in the form of a static or stillimage of the sample. The physical sample is preferably identified in thefield of view of an optical sensor in Block S310, which preferablytriggers Block S302 to capture the image of the physical sample, whereinthe image of the physical sample is only taken once the physical sampleis identified. Alternatively, the image of the sponge can be captured inBlock S302 and subsequently analyzed in Block S310 to identify thephysical sample visible therein.

The physical sample can be any of a surgical dressing, a surgical gauzesponge, a surgical towel, or any other absorbent textile used to collectblood or other bodily fluids. Like the first preferred method S100, asurgeon, nurse, anesthesiologist, gynecologist, soldier, paramedic, orother user can preferably use a machine, system, or device implementingthe third preferred method S300 to maintain a count of and to estimateextracorporeal blood volume in surgical towels, gauze sponges, or otherabsorbent textiles. By summing the estimated blood volumes acrossmultiple towels or gauze sponges, an estimated blood loss (EBL) for apatient can be estimated. The third preferred method S300 can thereforebe useful in a hospital setting, such as in a surgical operating room,or in a clinical setting, such as in a delivery room, or in any othersuitable setting.

Like the first preferred method S100, the third preferred method S300 ispreferably implemented in a handheld or mobile electronic device, suchas a native application or ‘app’ executing on a digital music player, aPDA, a smartphone, or a tablet computer. For example, a camera or otheroptical sensor integral with the electronic device can capture the imageof the sample in Block S302, a processor integral with the electronicdevice can perform Blocks S310, S320, and S330, and S340, and a displayintegral with the electronic device can display the sample count and theestimated blood volume in the physical sample and/or across multiplephysical samples in Block S360. In this variation, the electronic devicecan also communicate with a remote server that performs at least some ofBlocks S310, S320, S330, and S340. However, the third preferred methodS300 can be implemented in any other system, device, or combinationthereof.

As shown in FIG. 3A, Block S310 of the third preferred method S300recites identifying the physical sample in the field of view of theoptical sensor. The field of view of the optical sensor can be a staticimage or a video that was taken previously, wherein Block S310identifies the physical sample in the static image or videosubstantially after the image or video was taken. However, the field ofview of the optical sensor is can alternatively be a live feed from theoptical sensor, wherein Block S310 identifies the physical sample in thefield of view substantially in real time. The image is preferably acolor image captured by any of a digital color camera, an RGB camera, orany number of charge-coupled device (CCD) sensors, complimentarymetal-oxide-semiconductor (CMOS) active pixel sensors, or other opticalsensors of any other type. Furthermore, the optical sensor can capturethe image of the sample in any other form or across any other wavelengthor range of wavelengths in the visible spectrum, infrared spectrum, orany other spectrum.

Block S310 preferably implements machine vision to identify content inthe field of view as including or not including a suitable sample thatis surgical sponge gauze, towel, or dressing. In one variation of thethird preferred method S300, Block S310 uses edge detection to estimatethe perimeter of the physical sample visible in the field of view andthen determines a physical dimension of the physical sample, such aslength and width in inches, through gauging. The dimension of thephysical sample can be estimated by transforming the field of viewaccording to a known or anticipated distance or angle between theoptical sensor and the physical sample, by estimating distance and angleaccording to shadows or objects of known dimension in the field of view,by accessing data from an infrared, laser, sonic, or other range finderarranged proximal the optical sensor, or by any other suitable techniqueor device. By comparing the physical dimension(s) of the physical sampleto template samples in a library of suitable samples of knowndimension(s), Block S310 can determine both the presence, size, and/orand type of a physical sample in the field of view of the opticalsensor.

In another variation of the third preferred method S300, Block S310 alsoimplements edge detection to determine a boundary of the physical samplevisible in the field of view and subsequently removes substantially allof the field of view that is outside the estimated boundary of thephysical sample. Block S310 then performs image matching to comparegenerally the boundary of the physical sample visible in the field ofview with boundaries of template samples in a library of proper physicalsamples. In this variation, deviation in boundary path, color property,contrast with a background, or other property of the estimated physicalsample relative the template sample beyond a specified threshold canindicate that the sample in the field of view is not a suitable sample.

In a further variation of the third preferred method S300, Block S310implements pattern recognition and machine learning to determine thepresence and/or type of physical sample in the field of view of theoptical sensor. This variation preferably incorporates supervisedmachine learning, wherein Block S310 accesses a set of training datathat includes template images properly labeled as including or notincluding a suitable sample. A learning procedure then preferablytransforms the training data into generalized patterns to create a modelthat can subsequently be used to analyze the field of view of theoptical sensor an detect a proper physical sample shown therein.However, Block S310 can alternatively implement unsupervised learning orsemi-supervised learning (e.g. clustering, mixture of Gaussians,GrabCut) in which at least some of the training data has not beenlabeled. In this variation, Block S310 can further implement featureextraction, feature dimensionality reduction (e.g., principle componentanalysis (PCA)), feature selection, or any other suitable technique toprune redundant or irrelevant features from the field of view of theoptical sensor (or the image).

In any of the foregoing variations of the third preferred method S300,the third preferred method S300 preferably accepts an input indicativeof an improper identification of a physical sample in the field of view.The input, preferably provided by a surgeon, nurse, anesthesiologist,gynecologist, or other user, can indicate that the field of view doesinclude a suitable sample when Block S310 incorrectly determines thatthe field of view does not include a suitable sample. Also oralternatively, the input can indicate that the field of view does notinclude a suitable sample when Block S310 incorrectly determines thatthe field of view does include a suitable sample. This input is thenpreferably fed back into the set of training data, wherein the input isassumed correct, the field of view is labeled with the input, and thefield of view (or image) and input tag are added to the training set,such as in Block 332 shown in FIG. 3B. In the event that thedetermination of Block S310 is not corrected via such an input, thefield of view can also be fed back into the set of training data,wherein the determination of Block S310 is assumed correct absentcorrective input, the field of view is labeled with the determination ofBlock S310, and the field of view (or image) and determination tag areadded to the training set. Through this form of closed feedback, thetraining set can grow perpetually and continue to teach Block S310,which may substantially improve the machine-learning algorithm andimprove the accuracy of Block S310.

Block S310 can therefore implement any of segmentation, localization,edge detection, gauging, clustering, pattern recognition, templatematching, feature extraction, principle component analysis (PCA),feature dimensionality reduction, feature selection, thresholding,positioning, color analysis, closed feedback, or any other type ofmachine learning or machine vision. Such methods preferably compensatefor varying lighting conditions of the physical sponge, warping of thephysical sample (e.g., a wrinkle or warped sponge), warping of the imageof the physical sample (e.g., due to optical distortion caused by theoptical sensor), or any other inconsistency or variable common in usescenarios

Block S310 can additionally or alternatively function to identify otherrelevant objects, materials, or fluids in the field of view of theoptical sensor. For example, the aforementioned machine visiontechniques can again be similarly implemented in Block S310 to identifyblood droplets, drops, pools, or smears on a surgical tool, tray, table,wall, floor, or other surface. Such bloodies articles can also oralternatively be added to the sample count in Block S320 and/or analyzedin Blocks S330 and/or S340.

However, Block S310 can further include identifying additional physicalsamples in fields of view of the optical sensor and indexing the samplecounter for the identified additional physical samples, either in seriesbefore or after identifying the physical sample or substantiallysimultaneously while identifying the physical sample. In this variation,Block S310 can implement and one or more of the same or differentaforementioned methods or techniques to identify the additional physicalsamples in the field of view of the image.

As shown in FIG. 3B, one variation of the third preferred method S300includes Block S302, which recites capturing the image of the physicalsample. As described above, Block S302 can trigger Block 110, whereinBlock S302 captures the image and Block S310 subsequently identifies thepresence of a suitable sample in the field of view that is the image. Inthis variation, an input from a surgeon, a nurse, an anesthesiologist, agynecologist, or any other user can trigger Block S302. However, BlockS310 preferably triggers Block S302 when a suitable sample is identifiedin the field of view of the optical sensor. In this variation, a userpreferably places the physical sample within the field of view of theoptical sensor, Block S310 identifies the physical sample, and BlockS302 captures the image of the sample automatically. The physical sampleis preferably held at a substantially known angle between and/ordistance from the optical sensor such that a dimension of the physicalsample in the field of view can be estimated, such as through gaugingdescribed above.

The image of the physical sample captured in Block S302 is preferably acolor image of the physical sample against a background, wherein theimage is subsequently presented to a user on a digital display in BlockS360 with the sample count and the estimated blood volume in the sampledefining an augmented reality overlay. Alternatively, the image can be:a color image of the physical sample with the background removed; aninfrared image or black and white image; a fingerprint of the field ofview, such as with pointers or indicators of unique identifying featuresof the physical sample; or any other suitable type of image. The imageis preferably stored for later access, such as in the variation of thethird preferred method S300 that includes Block S380 in which theidentified physical sample is checked for a duplicate physical sampleidentified in a previous field of view or image. The image can be storedlocally, such as on a data storage module arranged within a handheldelectronic device performing at least some Blocks of the third preferredmethod S300, or remotely, such as in digital memory accessed through aremote server or a local network.

As shown in FIG. 3A, Block S320 of the third preferred method S300includes indexing the sample counter for the identified physical sampleidentified. The sample counter is preferably a cumulative counter ofsuccessive physical samples identified as a surgical dressing, asurgical gauze sponge, or a surgical towel. In Block S320, physicalsamples of various types can be tallied together in one group, thoughphysical samples of various types can alternatively be tallied inseparate groups. In this alternative, the groups can be definedaccording to genus, such as according to sample type including asurgical dressing group, a surgical gauze sponge group, or a surgicaltowel group. In this alternatively, the groups can also be definedaccording to species, such as according to manufacture or purposeincluding a Ray-Tec surgical gauze group and a laparotomy surgical gauzegroup. Furthermore, the sample count can include a tally of otherblood-related samples, such as blood drops, pools, or smears of certainsizes or estimated blood volumes, though the sample count can track thenumber of any other relevant physical sample of any other type.

As shown in FIG. 3B, one variation of the preferred method includesBlock S324, which recites receiving confirmation of identification ofthe physical sample. In this variation, Block S320 preferably indexesthe sample counter only for the physical sample that is confirmed.Sample confirmation is preferably provided by a user, such as through atouch-free gesture recognition or via a foot pedal, as described below.

The sample count is preferably displayed to a user, such as through adisplay in Block S360. The sample count is also preferably updated andstored on a local or remote hard drive or data storage device accessibleby the machine or system performing at least portions of the thirdpreferred method S300.

Block S330 of the third preferred method S300 recites extracting afeature from a portion of the field of the view of the optical sensor.Block S340 of the third preferred method S300 recites estimating theextracorporeal blood volume in a portion of the physical sample basedupon the extracted feature. Therefore, Blocks S330 and S340 of the thirdpreferred method S300 preferably cooperate to estimate extracorporealblood volume in the physical sample according to any one or more methodsof the first preferred method described above.

In one variation of the third preferred method S300, the field of viewof the optical segment or the image (the ‘image segment’) is staticallysegmented according to predefined segment size and/or shape, such as asquare ten-pixel by ten-pixel area. Alternatively, image segment can bedynamically segmented, such as according to redness, hue, saturation,shade, brightness, chroma, wavelength range, or any other metric ofcolor or light in the field of view or in the image. Each segment of theimage segment is preferably decomposed into separate color components(e.g., red, green, and blue), and for each color component, the absolutedifference in pixel intensity for the pixels in the image segment and atemplate image is calculated. The image segment is preferably thuscompared against available template images until a suitable match isfound. Each template image in the library of template images ispreferably an image of a master sample of known extracorporeal bloodvolume, hematocrit, red blood cell or hemoglobin volume, density, and/orany other suitable blood-related parameter or blood volume indicator.Specifically, each template image preferably includes information toinform the blood volume or blood volume indicator of the image segment.Furthermore, in Block S340, the blood volume indicator can be convertedinto a blood volume, a hemoglobin or red blood cell mass or volume, orother blood-related metric, such as correlated with an estimatedphysical dimension of a portion of the physical sample identified in theimage segment. Once each segment of the image or field of view is taggedwith a blood volume or indicator, the blood volume or indicator tags ofall image segments of the identified physical sample visible in theimage can be summed to estimate the total blood volume or indicator inthe physical sample.

The library of template images can additionally or alternatively be acolor palette, wherein each template image is a different colorindicative of a different blood volume or blood volume indicator, suchas rather than each template image being of a physical master sample ofknown blood volume or indicator. In this alternative, the library ispreferably a color palette for different types of absorbent surgicalsponge gauzes, dressings, and towels, wherein each color (i.e. templateimage) in a color palette for a particular type of physical sample isassociated with a particular blood volume or blood volume indicator. Inthis variation, the template image that is a color can be an image ofthe color or a numerical color identifier, such as a HEX code value(e.g., #FF0000, #A00000, #880000, etc.) or an RGB code value (e.g.,(255, 0, 0), (160, 0, 0), (190, 0, 0), etc.).

In this variation of the third preferred method S300, processing timerequired to find a template image match for each image segment can bereduced by avoiding comparison of each image segment with certaintemplate images substantially dissimilar from the image segment.Template images of master samples of surfaces, products, materials, ordimensions substantially dissimilar from that of the physical sample canbe excluded from comparison. Thresholding can also be used to removesubstantially irrelevant template images from the test pool. Forexample, template images with redness values (e.g., intensity, hue,saturation, shade, brightness, chroma, wavelength range) or physicaldimensions substantially dissimilar from that of the image segment canbe excluded from comparison. Tree searching can also be used to reduceprocessing time. However, template images can be grouped in the templatelibrary and selected or deselected from comparison with the imagesegment in any other way.

In another variation of the third preferred method S300, the imagelibrary is substantially large enough that the entire portion of theimage or field of associated with a proper physical sample is comparedagainst template images in the library, and the blood volume or bloodvolume indicator is directly estimated for the entire physical samplewithout segmentation.

In a further variation of the third preferred method S300, a rednessvalue is calculated for each image segment. Redness value can beintensity of a wavelength or composite intensity of a range ofwavelengths of light, redness hue, redness saturation, RGB code value(e.g., (0, 0, 0) through (255, 0, 0)) or any other suitable metric overthe image segment. Preferably, the image of the sample is decomposedinto distinct color spaces (e.g., red, green, and blue), wherein aredness value is calculated for the image segment in at least the redcolor space. The redness value of the image segment can then beconverted into a blood volume or blood volume indicator, such as througha lookup table, a regression model, a non-negative least-squaresalgorithm, or any other suitable algorithm, model, or method. Forexample, reflectance of oxygenated hemoglobin (HbO₂) can be correlatedwith certain wavelengths of light to substantially directly estimate thevolume or mass of hemoglobin in the portion of the physical sampleidentified in the image segment.

In still another variation of the third preferred method S300, the imageor field of view is not segmented, and a redness value is insteadcalculated for the entire portion of the image or field of viewcorrelated with the physical sample. The redness value can be an averageor weighted average of redness, hue, saturation, shade, brightness,chroma, wavelength range, or any other metric of color or light of theidentified image sample. As in a variation above, the blood volume orblood volume indicator for the entire portion of the physical sampleidentified in the field of view or in the image can be estimatedaccording to the redness value.

As shown in FIG. 3B, one variation of the third preferred method S300further includes Block S360, which recites displaying the estimatedextracorporeal blood volume in the physical sample and the sample count.This data is preferably presented on a digital display of a machine orsystem configured to perform the Blocks of the third preferred methodS300. This data can be presented in the form of an augmented realityoverlay on top of the static sample image depicted on the display, inthe form of a dynamic augmented reality overlay on top of a live videostream captured by the optical sensor and depicted on the display, inthe form of a table, chart, or diagram including the sample count andthe singular and/or cumulative estimated blood volumes in one or morephysical samples over a period of time or during a medical event oremergency, or in any other suitable form. This may be useful inestimating patient risk, in determining when to administer saline or toprovide a blood transfusion, in maintaining record of surgical events,and/or in estimating future blood-related events or patient needs. Otherblood-related metrics can also be estimated in the third preferredmethod S300 and presented in Block S360, such as blood spread rate,blood surface area, patient risk level, extracorporeal non-blood fluidvolume, or patient hemorrhage classification. However, the sample count,estimated blood volume(s), and/or any other blood-related metric orpatient information can be presented in any other way or form in BlockS360.

As shown in FIG. 3B, another variation of the third preferred methodS300 includes Block S380, which recites comparing the identifiedphysical sample against a set of past identified physical samples. Inthis variation, the sample counter is preferably indexed in Block S120only when the identified physical sample is determined to be uniqueamongst the set of past identified physical samples. The image of thesample, a fingerprint of the sample image, the extracted feature of theimage, or other identifying feature of the physical sample in the imagecan be compared with images, fingerprints, extracted features, and/orother identifying features of images of previous physical samples. BlockS380 preferably substantially therefore preferably prevents doublecounting of the physical sample in the sample count of Block S320 and/orprevents double counting of the estimated volume of blood in a totalextracorporeal blood volume estimate across multiple physical samples ortotal estimated blood loss of a patient. In Block S380, comparison ofthe sample image with a previous sample image can require imagefingerprinting or a scale, rotation, mirror, stretch or othertransformation of either the sample image or the previous sample image.Edge detection, pattern recognition, and/or other machine visiontechniques can additionally or alternatively be used to determine theuniqueness of bloodied regions of the physical sample visible in thesample image, relative to bloodied regions of a previous physicalsample. However, Block S380 can function in any other way to identifythe physical sample as shown in a previous sample image.

As shown in FIG. 3B, another variation of the third preferred methodS300 includes Block S390, which recites updating a digital medicalrecord of the patient with the estimated blood volume in the physicalsample. Block S190 can additionally or alternatively update the medicalrecord of the patient with the estimated blood volume across multiplephysical samples, the estimated blood loss of the patient, patient bloodloss trends, or any other relevant metric or data generated related tothe circulatory system of a patient. The digital medical record can bemaintained locally on a machine or system implementing the thirdpreferred method S300 or on a local network or remote server accessed bythe machine or system to retrieve, update, and/or upload the digitalmedical record.

The third preferred method S300 can further implement any one or moremethods, Blocks, or variations of the first preferred method S100.

4. Systems

As shown in FIGS. 8 and 9, a preferred system 100 for estimating theextracorporeal blood volume in a portion of a physical sample includes:an optical sensor 110, a processor 120, and a display 130. The opticalsensor 110 captures an image of the physical sample (the ‘sampleimage’). The processor 120 extracts a feature from a portion of an imageof the sample, tags the portion of the image of the sample with a bloodvolume indicator according to the extracted feature, and estimates theextracorporeal blood volume in at least a portion of the physicalsample, identified in the portion of the image of the sample, accordingto the blood volume indicator. The display 130 depicts the estimatedblood volume in at least the portion of the physical sample.

The system 100 preferably functions to estimate the volume of blood inthe sample by analyzing the sample image. The preferred system 100 isconfigured and/or adapted to perform one or more Blocks of the firstpreferred method S100. As described above, the sample is preferably anabsorbent surgical gauze sponge, though the sample can also be a tableor floor surface, a piece of clothing, an external skin surface orsurgical glove, a surgical implement, a fluid canister, or any othersurface or material. A surgeon, nurse, anesthesiologist, gynecologist,doctor, soldier, or other user can preferably use the system 100 toestimate blood volume in one sample and then sum the estimated bloodvolume in the sample with estimated blood volumes in other samples togenerate a total estimated blood loss (EBL) of a patient during asurgery, child birth, or other medical event or situation.

The preferred system 100 can alternatively function to estimate thecontent (e.g., volume, mass) of another blood-related parameter orextracorporeal blood volume indicator in the sample, such as hemoglobin,(HGB) or red blood cell (RBC) content of the sample. Furthermore, thepreferred system 100 can additionally or alternatively function todetect presence of blood in the sample, compute blood spread rate,calculate blood surface area, estimate patient risk level (e.g.,hypovolemic shock), and/or determine hemorrhage classification of thepatient. However, the preferred system 100 can provide any otherfunctionality, analyze any other image type or format, estimate anyother blood-related parameter, and calculate blood volume in thephysical sample in any other way.

As shown in FIG. 9, the preferred system 100 can be configured as ahandheld (e.g., mobile) electronic device, such as a smartphone ortablet running an image-based blood estimation application (or app) andincluding the camera 110, the processor 120, and the display 130.Alternatively, the components of the preferred system 100 can besubstantially discreet and distinct (i.e., not contained within a singlehousing). For example and as shown in FIG. 7, the optical sensor no canbe a camera substantially permanently arranged within an operating room,wherein the camera communicates with a local network or a remote server(including the processor 120) on which the sample image is analyzed(e.g., according to the method S100), and wherein a display 130 that isa computer monitor, a television, or a handheld (mobile) electronicdevice accesses and displays the output of the processor 120. However,the preferred system 100 can be of any other form or include any othercomponent.

The preferred system 100 can preferably be used in a variety ofsettings, including in a hospital setting, such as in a surgicaloperating room, in a clinical setting, such as in a delivery room, in amilitary setting, such as on a battlefield, or in a residential setting,such as aiding a consumer in monitoring blood loss due to menorrhagia(heavy menstrual bleeding) or epistaxis (nosebleeds). However, thepreferred system 100 can be used in any other setting.

The optical sensor 110 of the preferred system 100 functions to capturethe image of the physical sample. The optical sensor 110 preferablyimplements Block S102 of the preferred embodiment. The optical sensor110 is preferably a digital camera that captures a color sample image oran RGB camera that captures independent image components in the red,green, and blue fields. However, the optical sensor 110 can comprise anynumber of cameras, charge-coupled device (CCD) sensors, complimentarymetal-oxide-semiconductor (CMOS) active pixel sensors, or opticalsensors of any other type. Furthermore, the optical sensor 110 cancapture the sample image in any other form or across any otherwavelength or range of wavelengths in the visible spectrum, infraredspectrum, or any other spectrum.

The optical sensor 110 is preferably a camera arranged within a handheldelectronic device, as shown in FIG. 8. However, the optical sensor 110can also or alternatively be a camera or other sensor configured to bemounted on a pedestal for placement in an operating room, configured tobe mounted to a ceiling over an operating table, configured forattachment to a battlefield helmet of a field nurse, configured to mountto a standalone blood volume estimation system including the processor120, the display 130, and a staging tray that supports the physicalsample for imaging, or configured for placement in or attachment to anyother object or structure.

The processor 120 of the preferred system extracts a feature from aportion of an image of the sample, tags the portion of the image of thesample with a blood volume indicator according to the extracted feature,and estimates the extracorporeal blood volume in at least a portion ofthe physical sample, identified in the portion of the image of thesample, according to the blood volume indicator. The processor 120 canpreferably perform the Blocks of the first preferred method S100described above.

The processor 120 can be coupled to the optical sensor no, such as via awired connection (e.g., a trace on a shared PCB) or a wirelessconnection (e.g., a Wi-Fi or Bluetooth connection), such that theprocessor 120 can access the sample image captured by or visible in thefield of view of the optical sensor no. In one variation, the processor120 is arranged within a handheld electronic device that also containsthe optical sensor 110 and the display 130. In another variation, theprocessor 120 is a portion of or is tied to a remote server, whereinimage data from the optical sensor 110 is transmitted (e.g., via anInternet or local network connection) to the remote processor 120,wherein the processor 120 estimates the extracorporeal blood volume inat least the portion of the physical sample by analyzing the sampleimage, and wherein the blood volume estimate is transmitted to thedisplay 130.

In one variation of the preferred system 100 and as described above, theprocessor 120 can pair the portion of the sample image to the templateimage via template matching, and the template image is preferably onetemplate image in a library of template images. In another variation ofthe preferred system 100 and as described above, the processor 120parametrically generate the blood volume indicator based upon at leastone extract feature from the image of the sample. The processor 120 cantherefore be in communication with a local or remote data storagemodule, such as a hard drive in the handheld electronic device or amemory module of a remote server. The processor 120 can further uploadthe sample image for checking subsequent sample images against duplicateanalysis of the same physical sample, for example as described withreference to Block 180 of the first preferred method S100. Finally, theprocessor 120 can analyze different types of images (e.g., static,streaming, .MPEG, .JPG, .TIFF) and/or images from one or more distinctcameras or optical sensors.

The display 130 of the preferred system 100 preferably depicts theestimated blood volume in at least the portion of the physical sample.The display 130 is preferably arranged within the handheld electronicdevice (e.g., smartphone, tablet, personal data assistant) that alsocontains the optical sensor 110 and the processor 120, as shown in FIG.8. Alternatively, the display can be a computer monitor, a televisionscreen, or any other suitable display physically coextensive with anyother device, as shown in FIG. 7. The display 130 can therefore be anyof an LED, OLED, plasma, dot matrix, segment, e-ink, or retina display,a series of idiot lights corresponding to estimated blood volume, or anyother suitable type of display. Finally, the display 130 can be incommunication with the processor 120 via any of a wired and a wirelessconnection.

The display 130 can preferably perform at least Block S160 by depictingthe estimated blood volume in the portion of the physical sample, in thewhole of the physical sample, and/or across multiple physical samples.The blood volume estimate is preferably depicted in a common form, suchas “cc's” (cubic centimeters). As shown in FIG. 8, this data can bepresented in the form of a dynamic augmented reality overlay on top of alive video stream of the physical sample that is also depicted on thedisplay 130, wherein images from the optical sensor 110 are relayedsubstantially in real time, through the processor 120, to the display130. As shown in FIG. 8, the data can alternatively be presented in atable, chart, or graph depicting at least one of a time-elapsecumulative estimated blood volume across multiple samples analyzed overtime and individual blood volume estimates for each physical sample. Thedisplay 130 can also depict: previous sample images; warnings, such aspatient risk level (e.g., hypovolemic shock), or a hemorrhageclassification of the patient; or suggestions, such as begin bloodtransfusion. Any of these data, warnings, and/or suggestions can also bedepicted across multiple screens or made available for access on any oneof more displays.

As shown in FIG. 9, one variation of the preferred system 100 canfurther include a handheld housing 140 configured to contain the opticalsensor 110, the processor 120, and the display 130. The handheld housing140 with optical sensor no, processor 120, and display 130 can define ahandheld (mobile) electronic device capable of estimating blood volumein one or more physical samples in any number of suitable environments,such as in an operating room, a delivery room, a battlefield, a crimescene, and a home.

In another variation of the preferred system 100 shown in FIG. 8, thehousing 140 further contains a wireless communication module 150 thatcommunicates the estimated blood volume in the portion of the physicalsample to a remote server configured to store an electronic medicalrecord of a patient. The medical record is also preferably updated withestimated blood loss over time, patient risk level, hemorrhageclassification, and/or other blood-related metrics or blood volumeindicators. The patient medical record can therefore be updatedsubstantially automatically during a medical event, such as a surgery orchildbirth. The housing 140 is preferably of a medical-grade materialsuch that the system 100 that is a handheld electronic device issuitable for use in an operating room or other medical or clinicalsetting. The housing can therefore be medical-grade stainless steel,such as 316L stainless steel, a medical-grade polymer, such ashigh-density polyethylene (HDPE), or a medical-grade silicone rubber.However, the housing can be of any other material or combination ofmaterials.

As shown in FIG. 9, a variation of the preferred system 100 furtherincludes a foot pedal 160 accessible by a user to confirm or to refutethe identity of the physical sample. In this variation, the processor120 preferably indexes the sample count when the user confirms theidentity of the physical sample through the foot pedal 160. Additionallyor alternatively, the user can engage the foot pedal 160 to select anappropriate sample type (e.g., a surgical sponge gauze, a surgicaltowel, a surgical dressing), to scroll through previous sample images,or to control the preferred system 100 in any other way. The foot pedal160 therefore preferably includes at least two input regions that can beengaged by a foot of a user to manipulate the preferred system 100.

In one variation of the preferred system 100, the processor additionallyor alternatively compares a portion of the image of the sample with atemplate image of known blood volume indicator, tags the portion of theimage of the sample with a blood volume indicator according to thetemplate image that is matched to the portion of the image of thesample, and estimates the extracorporeal blood volume in at least aportion of the physical sample, associated with the portion of the imageof the sample, according to the blood volume indicator.

In a further variation of the preferred system 100, the processoradditionally or alternatively identifies the physical sample in theimage, indexes a sample counter for the identified physical sample,extracts a feature from a portion of the image, estimates theextracorporeal blood volume in a portion of the physical sample basedupon the extracted feature. The preferred system 100 can thereforeimplement the first preferred method, the third preferred method, and/orany combination or variation thereof.

The systems and methods of the preferred embodiments can be embodiedand/or implemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions are preferably executed by computer-executable componentspreferably integrated with the system, the optical sensor, theprocessor, the display, hardware/firmware/software elements of a systemor handheld electronic device, or any suitable combination thereof.Other systems and methods of the preferred embodiments can be embodiedand/or implemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions are preferably executed by computer-executable componentspreferably integrated by computer-executable components preferablyintegrated with apparatuses and networks of the type described above.The computer-readable medium can be stored on any suitable computerreadable media such as RAMs, ROMs, flash memory, EEPROMs, opticaldevices (CD or DVD), hard drives, floppy drives, or any suitable device.The computer-executable component is preferably a processor but anysuitable dedicated hardware device can (alternatively or additionally)execute the instructions.

As a person skilled in the art of estimating the extracorporeal bloodvolume in a physical sample will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for estimating the extracorporeal blood volume ina portion of a physical sample, the method comprising: comparing aportion of an image of the sample with a template image of knownextracorporeal blood volume indicator; tagging the portion of the imageof the sample with a blood volume indicator according to the templateimage that is matched to the portion of the image of the sample; andestimating the extracorporeal blood volume in at least a portion of thephysical sample, associated with the portion of the image of the sample,according to the blood volume indicator.
 2. The method of claim 1,further comprising identifying the physical sample in the image as atype of absorbent gauze sponge.
 3. The method of claim 2, furthercomprising indexing a sample counter for the physical sample that isidentified as an absorbent gauze sponge.
 4. The method of claim 3,further comprising identifying the physical sample as a physical sampleidentified in a previous image, wherein the sample counter is indexedwhen the physical sample is identified as unique amongst the physicalsamples in the previous images.
 5. The method of claim 2, whereincomparing the portion of the image of the sample with the template imagecomprises selecting the template image for comparison based upon thetype of absorbent gauze sponge of the physical sample.
 6. The method ofclaim 1, wherein estimating the extracorporeal blood volume in at leastthe portion of the sample comprises estimating the total extracorporealblood volume in the physical sample based upon aggregated blood volumeindicator tags of the portion of the image of the sample andsubstantially all other portions of the image.
 7. The method of claim 6,further comprising estimating patient blood loss by aggregating theestimated total extracorporeal blood volume in the physical sample andestimated total extracorporeal blood volumes in additional physicalsamples in additional images.
 8. The method of claim 1, whereincomparing the portion of the image of the sample with the template imagecomprises pairing the portion of the image of the sample and thetemplate image via template matching.
 9. The method of claim 8, whereincomparing the portion of the image of the sample with the template imagecomprises selecting the template image from a library of template imagesof known extracorporeal blood volume indicators.
 10. The method of claim8, wherein comparing the portion of the image of the sample with thetemplate image comprises calculating the absolute difference in pixelintensity for a plurality of pixels in each of the portion of the imageof the sample and the template image.
 11. The method of claim 1, whereincomparing the portion of the image of the sample with the template imagecomprises calculating a color intensity value in a color component spacefor each pixel of the portion of the image of the sample, generating ahistogram of the nearest-patch-centroid indices around each pixel, andpairing the portion of image of the sample with the template image basedupon a comparison of pixel centroid histograms.
 12. The method of claim1, wherein comparing the portion of the image of the sample with thetemplate image comprises accessing the template image that comprises acolor model paired with the blood volume indicator and generating theblood volume indicator tag that is a composite of the known blood volumeindicators of the template image and a second template image, whereinthe second template image comprises a second color model paired with asecond blood volume indicator.
 13. The method of claim 1, whereintagging the portion of the image of the sample with the blood volumeindicator comprises tagging the portion of the image of the sample withan estimated hemoglobin mass.
 14. The method of claim 13, whereinestimating the extracorporeal blood volume in at least the portion ofthe physical sample comprises estimating the extracorporeal blood volumein at least the portion of the physical sample according to thehemoglobin mass and an estimated hematocrit of blood in the physicalsample.
 15. The method of claim 1, further comprising capturing theimage through an optical sensor arranged within a mobile electronicdevice.
 16. The method of claim 15, further comprising displaying, on adisplay of the mobile electronic device, the estimated extracorporealblood volume in at least the portion of the physical sample.
 17. Themethod of claim 1, further comprising updating a digital medical recordof a patient with estimated blood volume in at least the portion of thephysical sample.
 18. The method of claim 1, wherein estimating theextracorporeal blood volume in at least the portion of the physicalsample comprises associating a physical dimension of the physical samplewith the portion of the image of the sample by transforming the portionof the image according to at least one of an estimated distance and anestimated angle between a capture origin of the image and the physicalsample.
 19. A system for determining the extracorporeal blood volume ina physical sample, the system comprising: an optical sensor thatcaptures an image of the physical sample; a processor that compares aportion of the image of the sample with a template image of known bloodvolume indicator, tags the portion of the image of the sample with ablood volume indicator according to the template image that is matchedto the portion of the image of the sample, and estimates theextracorporeal blood volume in at least a portion of the physicalsample, associated with the portion of the image of the sample,according to the blood volume indicator; and a display that depicts theestimated blood volume in at least the portion of the physical sample.20. The system of claim 19, wherein the optical sensor is a camera, andwherein the processor is further configured to determine at least one ofthe angle of the camera relative to the physical sample and the distancebetween the camera and the physical sample at the time of the image wascaptured.
 21. The system of claim 19, wherein the optical sensor isconfigured to be mounted overhead a surgical operating table.
 22. Thesystem of claim 19, further comprising a handheld housing configured tocontain the optical sensor, the processor, and the display.
 23. Thesystem of claim 22, further comprising a wireless communication modulethat communicates the estimated blood volume in the portion of thephysical sample to a remote server configured to store an electronicmedical record of a patient.
 24. The system of claim 19, furthercomprising a data storage module configured to store a library oftemplate images of known blood volume indicators, wherein the processoris configured to access the template image from the data storage modulefor comparison with the portion of the image of the sample.